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Task and Motion Planning
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이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
RH20T: A Comprehensive Robotic Dataset for Learning Diverse Skills in One-Shot
DiMSam: Diffusion Models as Samplers for Task and Motion Planning under Partial Observability
AutoTAMP: Autoregressive Task and Motion Planning with LLMs as Translators and Checkers
Imitating Task and Motion Planning with Visuomotor Transformers
FurnitureBench: Reproducible Real-World Benchmark for Long-Horizon Complex Manipulation
Optimal task and motion planning and execution for human-robot multi-agent systems in dynamic environments
Search-Based Task and Motion Planning for Hybrid Systems: Agile Autonomous Vehicles
Orbit: A Unified Simulation Framework for Interactive Robot Learning Environments
Sequence-Based Plan Feasibility Prediction for Efficient Task and Motion Planning
Policy-Guided Lazy Search with Feedback for Task and Motion Planning
LEAGUE: Guided Skill Learning and Abstraction for Long-Horizon Manipulation
STAP: Sequencing Task-Agnostic Policies
Hypergraph-based Multi-Robot Task and Motion Planning
Learning Neuro-Symbolic Skills for Bilevel Planning
Learning Symbolic Operators: A Neurosymbolic Solution for Autonomous Disassembly of Electric Vehicle Battery
Accelerating Integrated Task and Motion Planning with Neural Feasibility Checking
Representation, learning, and planning algorithms for geometric task and motion planning
Cooperative Task and Motion Planning for Multi-Arm Assembly Systems
Guided Imitation of Task and Motion Planning
Learning to Search in Task and Motion Planning with Streams
JEDAI: A System for Skill-Aligned Explainable Robot Planning
Integrated Task and Motion Planning for Safe Legged Navigation in Partially Observable Environments
Task Allocation for Multi-Robot Task and Motion Planning: a case for Object Picking in Cluttered Workspaces
An Anytime Hierarchical Approach for Stochastic Task and Motion Planning
Long-Horizon Manipulation of Unknown Objects via Task and Motion Planning with Estimated Affordances
The ThreeDWorld Transport Challenge: A Visually Guided Task-and-Motion Planning Benchmark for Physically Realistic Embodied AI
Extended Tree Search for Robot Task and Motion Planning
Learning Symbolic Operators for Task and Motion Planning
A Long Horizon Planning Framework for Manipulating Rigid Pointcloud Objects
Integrated Task and Motion Planning
Planning with Learned Object Importance in Large Problem Instances using Graph Neural Networks
CAMPs: Learning Context-Specific Abstractions for Efficient Planning in Factored MDPs
Deep Visual Reasoning: Learning to Predict Action Sequences for Task and Motion Planning from an Initial Scene Image
Learning compositional models of robot skills for task and motion planning
Flexible and Efficient Long-Range Planning Through Curious Exploration
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.